Quick and Accurate False Data Detection in Mobile Crowd Sensing
Quick and Accurate False Data Detection in Mobile Crowd Sensing
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DOI：
10.1109/tnet.2020.2982685
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Xiaocan Li;Kun Xie;Xin Wang;Gaogang Xie;Dongliang Xie;Zhenyu Li;Jigang Wen;Zulong Diao;Tian Wang
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文献类型：
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作者：
Xiaocan Li;Kun Xie;Xin Wang;Gaogang Xie;Dongliang Xie;Zhenyu Li;Jigang Wen;Zulong Diao;Tian Wang
移动人群感知(MCS)中的攻击、故障和严重的通信/系统条件使得虚假数据检测成为一个关键问题。通过观察一般监测数据固有的低维性和虚假数据的稀疏性，可以基于正常数据和异常数据的分离来进行虚假数据检测。虽然现有的基于直接稳健矩阵分解(DRMF)的分离算法被证明是有效的，但当数据矩阵很大时，需要迭代地执行奇异值分解(SVD)来进行低阶矩阵逼近将导致极高的累积计算代价。在这项工作中，我们从DRMF的实验研究中观察到了快速虚假数据定位的特征，在此基础上，我们提出了一种智能的轻量级低阶和虚假矩阵分离算法(LightLRFMS)，该算法可以重用之前的矩阵分解结果来推导出当前迭代步骤的结果。根据数据损坏的类型，随机或连续/大量，我们设计了两个版本的LightLRFMS。从理论上验证了LightLRFMS只需要一轮奇异值分解计算，因此具有很低的计算代价。我们已经使用PM2.5空调跟踪和道路交通跟踪进行了广泛的实验。实验结果表明，LightLRFMS算法具有与DRMF相同的最高检测精度，但由于其较低的计算代价，检测速度提高了20倍，具有很好的虚假数据检测性能。
The attacks, faults, and severe communication/system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.